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Particle dispersion modeling in ventilated room using artificial neural network

Nuclear Science and Techniques 2017年第1期28卷 27-35页

作者： Athmane Gheziel salah hanini Brahim Mohamedi Abdelrahmane Ararem Birine Nuclear Research Centre P.B. 180C.P. 17200 Ain Oussera Algeria LBMPT Yahia Fare`s University 26000 Me′de′a Algeria

Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a modeling approach based on the artificial neural network(ANN) to describe spatial distribution of the particles concentration in an indoor *** study was performed for a stationary flow *** database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental *** perceptron-type neural network(MLP-ANN) model was developed to map the relation between the input variables and the *** training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm(TrainCgf).The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD *** developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD *** average absolute error given by the ANN model does not reach 5%against 18%by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.

关键词： Numerical simulation Computational fluid dynamic Artificial neural network Spatial distribution Particle concentration Indoor environment

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Simulation of nucleate boiling under ANSYS-FLUENT code by using RPI model coupling with artificial neural networks

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Nuclear Science and Techniques 2015年第4期26卷 95-101页

作者： Brahim Mohamedi salah hanini Abdelrahmane Ararem Nacim Mellel Birine Nuclear Research Center B.P.180 Ain Oussera 17200 Algrie LBMPT Dr Yahia Fars University Birine Nuclear Research Center B.P.180 Ain Oussera 17200 Algri

The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear ***,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal *** to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial *** that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver *** technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated *** is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this *** fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.

关键词：人工神经网络神经网络模拟耦合模型过冷沸腾 RPI 代码 IAPWS-IF97 CFD软件

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari salah hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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